Previous research in the simulation modeling for lifting tasks showed promising results. The current simulation model developed at Texas Tech University can simulate lifting movement from floor to knuckle (FK) quite well. The purpose of this proposed research is to extend this modeling effort to apply to various lifting conditions such as lifting floor to shoulder lifting (FS) and knuckle to shoulder lifting (KS). In addition the proposed model will be able to simulate lifting action for females in addition to males (of which the current model is capable). In order to accomplish the development of this simulation model, data concerning the pull force (a force needed initially to pick up the inertia is needed, Danz 1991). Therefore the proposed research is divided into three phases. Phase I will be experimental in nature and will focus on collecting two types of data. The first is the pull force data mentioned above which is needed for the simulation model. The second set of data needed is the x and y coordinates of targets mounted on the body from which the kinematics and kinetics of the motions can be estimated. From these kinematics and kinetics, upper and lower limits will be established for model development and model validation. Phase II is devoted to the simulation model development using two approaches: one is a filtering method and the other is a general gradient reduced method. Both of these techniques, although different, show promise in being able to achieve an optimum path of motion based on a weighted objective function. The objective function is based on the assumption that in all likelihood the body will select a path of motion to minimize a "cost function". Phase III will be concerned with the validation of the simulation model. This will be accomplished using the data collected in Phase I plus additional data collected for this purpose. Validation consists of comparisons between what the model predicts and what the subject actually does in terms of the kinematics and kinetics. Three techniques for validation are recommended: (1) sum of squares, (2) pairs of concordance, and (3) Theil's inequality coefficient.